{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Autoregressive time series forecasting with the Luna Dataset\n",
        "\n",
        "This example shows how to train an autoregressive time-series model using the Luna dataset. Autoregressive is based on a very simple idea that observation from the current time steps is decided by the observation from previous steps, and is very suitable for time series data that presents a strong seasonal pattern.\n",
        "\n",
        "In this notebook you will learn:\n",
        "- How to transform a univariante-forecasting into a regression problem.\n",
        "- How to run hyper-parameter optimization and search for the best regression model for your data using built-in `AutoMLExperiment` class.\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install NuGet packages for training ML.NET models and plotting:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div><div><strong>Restore sources</strong><ul><li><span>https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json</span></li></ul></div><div></div><div><strong>Installed Packages</strong><ul><li><span>Microsoft.Data.Analysis, 0.20.0-preview.22458.1</span></li><li><span>Microsoft.ML.AutoML, 0.20.0-preview.22458.1</span></li><li><span>Plotly.NET.CSharp, 0.0.1</span></li><li><span>Plotly.NET.Interactive, 3.0.2</span></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "Loading extensions from `Microsoft.ML.AutoML.Interactive.dll`"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "Loading extensions from `Plotly.NET.Interactive.dll`"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "Loading extensions from `Microsoft.Data.Analysis.Interactive.dll`"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "// using nightly-build\n",
        "#i \"nuget:https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json\"\n",
        "#r \"nuget: Plotly.NET.Interactive, 3.0.2\"\n",
        "#r \"nuget: Plotly.NET.CSharp, 0.0.1\"\n",
        "#r \"nuget: Microsoft.ML.AutoML, 0.20.0-preview.22458.1\"\n",
        "#r \"nuget: Microsoft.Data.Analysis, 0.20.0-preview.22458.1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [],
      "source": [
        "using static Microsoft.DotNet.Interactive.Formatting.PocketViewTags;\n",
        "using Microsoft.Data.Analysis;\n",
        "using System;\n",
        "using System.IO;\n",
        "using Microsoft.ML;\n",
        "using Microsoft.ML.AutoML;\n",
        "using Microsoft.ML.Data;\n",
        "using Plotly.NET;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Import Dataset\n",
        "\n",
        "Luna is a time-series dataset which records the metric of hourly-active usage of an internal-used service on Azure. It has two columns: `DateTime` and `load`, where `load` column records the metric of hourly-active usage. Luna shows a strong weekly seasonal pattern given its nature as an Azure service, and a slow trend (growth with time) as well. Those features allow you to build a forecasting model to predict the next hour load metric to adjust the size of computing power accordingly.\n",
        "\n",
        "In the code block below, we show how to\n",
        "- load dataset\n",
        "- transform a time-series forecasting problem into a regression problem via using the last _N_ hour as feature and the current `load` as label. We do that because there are more regressors available in ML.Net, which leaves room for AutoML to optimize with. However, based on our practice, you can also achieve pretty close result via sweeping over parameters of `SSA`. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Download or Locate Data\n",
        "The following code tries to locate the data file in a few known locations or it will download it from the known GitHub location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [],
      "source": [
        "using System;\n",
        "using System.IO;\n",
        "using System.Net;\n",
        "\n",
        "string EnsureDataSetDownloaded(string fileName)\n",
        "{\n",
        "\n",
        "\t// This is the path if the repo has been checked out.\n",
        "\tvar filePath = Path.Combine(Directory.GetCurrentDirectory(),\"data\", fileName);\n",
        "\n",
        "\tif (!File.Exists(filePath))\n",
        "\t{\n",
        "\t\t// This is the path if the file has already been downloaded.\n",
        "\t\tfilePath = Path.Combine(Directory.GetCurrentDirectory(), fileName);\n",
        "\t}\n",
        "\n",
        "\tif (!File.Exists(filePath))\n",
        "\t{\n",
        "\t\tusing (var client = new WebClient())\n",
        "\t\t{\n",
        "\t\t\tclient.DownloadFile($\"https://raw.githubusercontent.com/dotnet/csharp-notebooks/main/machine-learning/data/{fileName}\", filePath);\n",
        "\t\t}\n",
        "\t\tConsole.WriteLine($\"Downloaded {fileName}  to : {filePath}\");\n",
        "\t}\n",
        "\telse\n",
        "\t{\n",
        "\t\tConsole.WriteLine($\"{fileName} found here: {filePath}\");\n",
        "\t}\n",
        "\n",
        "\treturn filePath;\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Luna.csv found here: c:\\Users\\xiaoyuz\\source\\repos\\csharp-notebooks\\machine-learning\\data\\Luna.csv\r\n"
          ]
        }
      ],
      "source": [
        "var dataPath =  EnsureDataSetDownloaded(@\"Luna.csv\");\n",
        "\n",
        "// use previous 2 weeks data\n",
        "var df = DataFrame.LoadCsv(dataPath);\n",
        "var prevWindows = Enumerable.Range(1, 24 * 7 * 2);\n",
        "var loads = df[\"load\"].Cast<float?>();\n",
        "foreach(var i in prevWindows)\n",
        "{\n",
        "    var columnName = $\"prev_{i}h\";\n",
        "    df[columnName] = DataFrameColumn.Create(columnName, Enumerable.Repeat<float?>(null, i).Concat(loads).SkipLast(i));\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plotting last three weeks of Luna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    <div id=\"0245539b-8319-404d-8346-aa5a21f44d90\"><!-- Plotly chart will be drawn inside this DIV --></div>\r\n",
              "<script type=\"text/javascript\">\r\n",
              "\r\n",
              "            var renderPlotly_0245539b8319404d8346aa5a21f44d90 = function() {\r\n",
              "            var fsharpPlotlyRequire = requirejs.config({context:'fsharp-plotly',paths:{plotly:'https://cdn.plot.ly/plotly-2.6.3.min'}}) || require;\r\n",
              "            fsharpPlotlyRequire(['plotly'], function(Plotly) {\r\n",
              "\r\n",
              "            var data = 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              "            var config = {\"responsive\":true};\r\n",
              "            Plotly.newPlot('0245539b-8319-404d-8346-aa5a21f44d90', data, layout, config);\r\n",
              "});\r\n",
              "            };\r\n",
              "            if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) {\r\n",
              "                var script = document.createElement(\"script\");\r\n",
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              "\n",
              "    \n",
              "</div>    \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "var lastThreeWeek = df[\"load\"].Cast<float>().TakeLast(7 * 24 * 3);\n",
        "var x = Enumerable.Range(0, lastThreeWeek.Count());\n",
        "var line = Chart2D.Chart.Line<int, float, string>(x, lastThreeWeek, Name: \"load\");\n",
        "line.Display();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### A glance at dataset\n",
        "We use hourly intervals from the previous 2 weeks as features and current value `load` as label to predict. In doing so, it reframes the forecasting problem into a regression problem, which has more trainer options available in ML.NET. You can either call `Tail(N)` to get the last _N_ rows of data, or `Head(N)` to see the first _N_ rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table 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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.Tail(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Construct training pipeline\n",
        "The following code shows how to construct an ML.NET training pipeline which concatenates all the feature columns with the `prev` prefix into a single feature vector and provides it as input to the regression trainer\n",
        "\n",
        "Notice that the pipeline calls `mlContext.Auto().Regression` in the end, which returns all available regressors with pre-defined search spaces. Then we can pass this pipeline into `AutoMLExperiment` for hyperparameter optimization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [],
      "source": [
        "var rowCount = df.Rows.Count();\n",
        "var evaluateCount = 24*7;\n",
        "var trainDf = df.Head(rowCount -evaluateCount);\n",
        "var evaluateDf = df.Tail(evaluateCount);\n",
        "var mlContext = new MLContext();\n",
        "\n",
        "var featureColumns = df.Columns.Select(c => c.Name)\n",
        "                        .Where(c => c!=\"load\");\n",
        "// Append the trainer to the data processing pipeline\n",
        "var pipeline = mlContext.Transforms.Concatenate(@\"Features\", featureColumns.ToArray())\n",
        "                    .Append(mlContext.Auto().Regression(labelColumnName: \"load\", useLbfgs: false, useSdca: false, useFastForest: false, useFastTree: false, useLgbm: true));"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Run Hyper-parameter optimization using AutoMLExperiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div><h3>Best Trial</h3><p>Id: 1</p><p>Trainer: LightGbmRegression</p><p>Parameters: {\r\n",
              "  &quot;_pipeline_&quot;: {\r\n",
              "    &quot;_SCHEMA_&quot;: &quot;e0 * e1&quot;,\r\n",
              "    &quot;e0&quot;: {},\r\n",
              "    &quot;e1&quot;: {\r\n",
              "      &quot;NumberOfLeaves&quot;: 4,\r\n",
              "      &quot;MinimumExampleCountPerLeaf&quot;: 20,\r\n",
              "      &quot;LearningRate&quot;: 0.9999997766729865,\r\n",
              "      &quot;NumberOfTrees&quot;: 641,\r\n",
              "      &quot;SubsampleFraction&quot;: 0.46873628525675043,\r\n",
              "      &quot;MaximumBinCountPerFeature&quot;: 310,\r\n",
              "      &quot;FeatureFraction&quot;: 0.99999999,\r\n",
              "      &quot;L1Regularization&quot;: 7.082058270068566E-10,\r\n",
              "      &quot;L2Regularization&quot;: 0.9999997766729865,\r\n",
              "      &quot;LabelColumnName&quot;: &quot;load&quot;,\r\n",
              "      &quot;FeatureColumnName&quot;: &quot;Features&quot;\r\n",
              "    }\r\n",
              "  }\r\n",
              "}</p><h3>Active Trial</h3><p>Id: 2</p><p>Trainer: LightGbmRegression</p><p>Parameters: {\r\n",
              "  &quot;_pipeline_&quot;: {\r\n",
              "    &quot;_SCHEMA_&quot;: &quot;e0 * e1&quot;,\r\n",
              "    &quot;e0&quot;: {},\r\n",
              "    &quot;e1&quot;: {\r\n",
              "      &quot;NumberOfLeaves&quot;: 184,\r\n",
              "      &quot;MinimumExampleCountPerLeaf&quot;: 24,\r\n",
              "      &quot;LearningRate&quot;: 0.18857745129621922,\r\n",
              "      &quot;NumberOfTrees&quot;: 305,\r\n",
              "      &quot;SubsampleFraction&quot;: 0.3352310256012691,\r\n",
              "      &quot;MaximumBinCountPerFeature&quot;: 674,\r\n",
              "      &quot;FeatureFraction&quot;: 0.99999999,\r\n",
              "      &quot;L1Regularization&quot;: 1.7576217979527973E-08,\r\n",
              "      &quot;L2Regularization&quot;: 0.40154721350144645,\r\n",
              "      &quot;LabelColumnName&quot;: &quot;load&quot;,\r\n",
              "      &quot;FeatureColumnName&quot;: &quot;Features&quot;\r\n",
              "    }\r\n",
              "  }\r\n",
              "}</p></div><div><h3>Plot Metrics over Trials</h3></div>\n",
              "<div>\n",
              "    <div id=\"e7454942-73a7-4b35-9b95-eb4808c01d74\"><!-- Plotly chart will be drawn inside this DIV --></div>\r\n",
              "<script type=\"text/javascript\">\r\n",
              "\r\n",
              "            var renderPlotly_e745494273a74b359b95eb4808c01d74 = function() {\r\n",
              "            var fsharpPlotlyRequire = requirejs.config({context:'fsharp-plotly',paths:{plotly:'https://cdn.plot.ly/plotly-2.6.3.min'}}) || require;\r\n",
              "            fsharpPlotlyRequire(['plotly'], function(Plotly) {\r\n",
              "\r\n",
              "            var data = [{\"type\":\"scatter\",\"name\":\"Plot Metrics over Trials.\",\"mode\":\"markers\",\"x\":[0,1],\"y\":[285956.59963799047,170234.92163403527],\"marker\":{},\"line\":{},\"showlegend\":false}];\r\n",
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              "\n",
              "    \n",
              "</div>    \n",
              "<div><h3>All Trials Table</h3></div><table id=\"table_637983334413366535\"><thead><tr><th><i>index</i></th><th>Trial</th><th>Metric</th><th>Trainer</th><th>Parameters</th></tr></thead><tbody><tr><td><i><div class=\"dni-plaintext\">0</div></i></td><td><div class=\"dni-plaintext\">0</div></td><td><div class=\"dni-plaintext\">285956.6</div></td><td>LightGbmRegression</td><td>{&quot;_pipeline_&quot;:{&quot;_SCHEMA_&quot;:&quot;e0 * e1&quot;,&quot;e0&quot;:{},&quot;e1&quot;:{&quot;NumberOfLeaves&quot;:4,&quot;MinimumExampleCountPerLeaf&quot;:20,&quot;LearningRate&quot;:1,&quot;NumberOfTrees&quot;:4,&quot;SubsampleFraction&quot;:1,&quot;MaximumBinCountPerFeature&quot;:254,&quot;FeatureFraction&quot;:1,&quot;L1Regularization&quot;:2E-10,&quot;L2Regularization&quot;:1,&quot;LabelColumnName&quot;:&quot;load&quot;,&quot;FeatureColumnName&quot;:&quot;Features&quot;}}}</td></tr><tr><td><i><div class=\"dni-plaintext\">1</div></i></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">170234.92</div></td><td>LightGbmRegression</td><td>{&quot;_pipeline_&quot;:{&quot;_SCHEMA_&quot;:&quot;e0 * e1&quot;,&quot;e0&quot;:{},&quot;e1&quot;:{&quot;NumberOfLeaves&quot;:4,&quot;MinimumExampleCountPerLeaf&quot;:20,&quot;LearningRate&quot;:0.9999997766729865,&quot;NumberOfTrees&quot;:641,&quot;SubsampleFraction&quot;:0.46873628525675043,&quot;MaximumBinCountPerFeature&quot;:310,&quot;FeatureFraction&quot;:0.99999999,&quot;L1Regularization&quot;:7.082058270068566E-10,&quot;L2Regularization&quot;:0.9999997766729865,&quot;LabelColumnName&quot;:&quot;load&quot;,&quot;FeatureColumnName&quot;:&quot;Features&quot;}}}</td></tr></tbody></table>"
            ]
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "// Configure AutoML\n",
        "var trainTestSplit = mlContext.Data.TrainTestSplit(trainDf, 0.1);\n",
        "\n",
        "// NotebookMonitor plots trials and show best run nicely in notebook output cell.\n",
        "var monitor = new NotebookMonitor(pipeline);\n",
        "\n",
        "var experiment = mlContext.Auto().CreateExperiment()\n",
        "                    .SetPipeline(pipeline)\n",
        "                    .SetTrainingTimeInSeconds(60)\n",
        "                    .SetDataset(trainTestSplit.TrainSet, trainTestSplit.TestSet)\n",
        "                    .SetRegressionMetric(RegressionMetric.RootMeanSquaredError, \"load\", \"Score\")\n",
        "\t\t\t\t\t.SetMonitor(monitor);\n",
        "\n",
        "// Configure Visualizer\t\t\t\n",
        "monitor.SetUpdate(monitor.Display());\n",
        "\n",
        "// Start Experiment\n",
        "var res = await experiment.RunAsync();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluate model using test dataset\n",
        "The following code shows how to use the best model produced by hyper-parameter optimization from `AutoMLExperiment` to predict the Luna `load` for next two weeks, then compare it with the actual `load` and calculate regression metric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table><thead><tr><th>MeanAbsoluteError</th><th>MeanSquaredError</th><th>RootMeanSquaredError</th><th>LossFunction</th><th>RSquared</th></tr></thead><tbody><tr><td><div class=\"dni-plaintext\">153566.89262462797</div></td><td><div class=\"dni-plaintext\">38769258423.02824</div></td><td><div class=\"dni-plaintext\">196899.10721744839</div></td><td><div class=\"dni-plaintext\">38769258436.76302</div></td><td><div class=\"dni-plaintext\">0.9690737438487391</div></td></tr></tbody></table>"
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "var model = res.Model;\n",
        "var eval = model.Transform(evaluateDf); // we should use an unseen dataset though.\n",
        "var metric = mlContext.Regression.Evaluate(eval, \"load\");\n",
        "evaluateDf[\"predicted\"] = DataFrameColumn.Create(\"predicted\", eval.GetColumn<float>(\"Score\"));\n",
        "\n",
        "// print metric\n",
        "metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot both predicted and truth sonar in test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "dotnet_interactive": {
          "language": "csharp"
        },
        "vscode": {
          "languageId": "dotnet-interactive.csharp"
        }
      },
      "outputs": [
        {
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      ],
      "source": [
        "var predicted = evaluateDf[\"predicted\"].Cast<float>();\n",
        "var truth = evaluateDf[\"load\"].Cast<float>();\n",
        "var X = Enumerable.Range(0, truth.Count());\n",
        "var predictedChart = Chart2D.Chart.Line<int, float, string>(X, predicted, Name: \"predicted\");\n",
        "var truthChart = Chart2D.Chart.Line<int, float, string>(X, truth, Name: \"truth\");\n",
        "var combineChart = Chart.Combine(new[]{ predictedChart, truthChart});\n",
        "combineChart.Display()"
      ]
    }
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